fix initializer mode

examples/ResNet/cifar10-resnet.py

@@ -74,7 +74,7 @@ class Model(ModelDesc):
 
         with argscope([Conv2D, AvgPooling, BatchNorm, GlobalAvgPooling], data_format='NCHW'), \
                 argscope(Conv2D, nl=tf.identity, use_bias=False, kernel_shape=3,
-                         W_init=tf.variance_scaling_initializer(scale=2.0, mode='FAN_OUT')):
+                         W_init=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
             l = Conv2D('conv0', image, 16, nl=BNReLU)
             l = residual('res1.0', l, first=True)
             for k in range(1, self.n):

examples/ResNet/resnet_model.py

@@ -115,7 +115,7 @@ def resnet_group(l, name, block_func, features, count, stride):
 
 def resnet_backbone(image, num_blocks, group_func, block_func):
     with argscope(Conv2D, nl=tf.identity, use_bias=False,
-                  W_init=tf.variance_scaling_initializer(scale=2.0, mode='FAN_OUT')):
+                  W_init=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
         logits = (LinearWrap(image)
                   .Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
                   .MaxPooling('pool0', shape=3, stride=2, padding='SAME')

examples/Saliency/CAM-resnet.py

@@ -47,7 +47,7 @@ class Model(ModelDesc):
         defs, block_func = cfg[DEPTH]
 
         with argscope(Conv2D, nl=tf.identity, use_bias=False,
-                      W_init=tf.variance_scaling_initializer(scale=2.0, mode='FAN_OUT')), \
+                      W_init=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')), \
                 argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm], data_format='NCHW'):
             convmaps = (LinearWrap(image)
                         .Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)